import { apiClient } from './client'
import { io, Socket } from 'socket.io-client'

export interface ProcessingOptions {
  model?: 'rembg' | 'u2net' | 'deeplab' | 'custom'
  quality?: 'low' | 'medium' | 'high' | 'ultra'
  returnMask?: boolean
  edgeRefinement?: number
  feather?: number
  tolerance?: number
  preserveDetails?: boolean
}

export interface ProcessingResult {
  id: string
  image: string
  mask?: string
  metadata: {
    width: number
    height: number
    format: string
    processingTime: number
  }
}

export interface BatchJob {
  id: string
  status: 'pending' | 'processing' | 'completed' | 'failed'
  progress: number
  totalFiles: number
  processedFiles: number
  results?: ProcessingResult[]
  error?: string
  createdAt: string
  completedAt?: string
}

class ProcessingAPI {
  private socket: Socket | null = null
  private listeners: Map<string, Set<Function>> = new Map()

  /**
   * Process a single image
   */
  async processImage(
    file: File,
    options: ProcessingOptions = {},
    onProgress?: (progress: number) => void
  ): Promise<ProcessingResult> {
    const formData = new FormData()
    formData.append('file', file)
    formData.append('options', JSON.stringify(options))

    return apiClient.upload<ProcessingResult>(
      '/api/v1/process/remove-background',
      file,
      onProgress
    )
  }

  /**
   * Process multiple images in batch
   */
  async processBatch(
    files: File[],
    options: ProcessingOptions = {}
  ): Promise<BatchJob> {
    const formData = new FormData()
    files.forEach((file) => formData.append('files', file))
    formData.append('options', JSON.stringify(options))

    const response = await apiClient.post<BatchJob>('/api/v1/process/batch', formData, {
      headers: { 'Content-Type': 'multipart/form-data' },
    })

    // Connect to WebSocket for real-time updates
    this.connectWebSocket(response.id)

    return response
  }

  /**
   * Get batch job status
   */
  async getJobStatus(jobId: string): Promise<BatchJob> {
    return apiClient.get<BatchJob>(`/api/v1/process/jobs/${jobId}`)
  }

  /**
   * Cancel a batch job
   */
  async cancelJob(jobId: string): Promise<void> {
    await apiClient.post(`/api/v1/process/jobs/${jobId}/cancel`)
  }

  /**
   * Replace background with a new one
   */
  async replaceBackground(
    imageId: string,
    background: string | File
  ): Promise<ProcessingResult> {
    const formData = new FormData()
    formData.append('imageId', imageId)
    
    if (background instanceof File) {
      formData.append('background', background)
    } else {
      formData.append('backgroundUrl', background)
    }

    return apiClient.post<ProcessingResult>(
      '/api/v1/process/replace-background',
      formData,
      { headers: { 'Content-Type': 'multipart/form-data' } }
    )
  }

  /**
   * Apply AI-powered enhancements
   */
  async enhanceImage(
    imageId: string,
    enhancements: {
      sharpness?: number
      contrast?: number
      brightness?: number
      saturation?: number
      denoise?: boolean
      upscale?: 2 | 4
    }
  ): Promise<ProcessingResult> {
    return apiClient.post<ProcessingResult>('/api/v1/process/enhance', {
      imageId,
      enhancements,
    })
  }

  /**
   * Generate AI background
   */
  async generateBackground(
    prompt: string,
    style?: 'realistic' | 'artistic' | 'abstract' | 'gradient'
  ): Promise<{ id: string; url: string }> {
    return apiClient.post('/api/v1/backgrounds/generate', {
      prompt,
      style,
    })
  }

  /**
   * Smart edge detection and refinement
   */
  async refineEdges(
    imageId: string,
    maskId: string,
    options: {
      mode: 'hair' | 'fur' | 'smooth' | 'detailed'
      strength: number
    }
  ): Promise<ProcessingResult> {
    return apiClient.post<ProcessingResult>('/api/v1/process/refine-edges', {
      imageId,
      maskId,
      ...options,
    })
  }

  /**
   * Connect to WebSocket for real-time updates
   */
  private connectWebSocket(jobId: string) {
    if (this.socket?.connected) {
      this.socket.emit('subscribe', { jobId })
      return
    }

    const wsUrl = process.env.NEXT_PUBLIC_WS_URL || 'ws://localhost:8000'
    this.socket = io(wsUrl, {
      transports: ['websocket'],
      query: { jobId },
    })

    this.socket.on('connect', () => {
      console.log('WebSocket connected')
      this.socket?.emit('subscribe', { jobId })
    })

    this.socket.on('job:progress', (data) => {
      this.emit('progress', data)
    })

    this.socket.on('job:complete', (data) => {
      this.emit('complete', data)
      this.disconnectWebSocket()
    })

    this.socket.on('job:error', (data) => {
      this.emit('error', data)
      this.disconnectWebSocket()
    })

    this.socket.on('disconnect', () => {
      console.log('WebSocket disconnected')
    })
  }

  /**
   * Disconnect WebSocket
   */
  private disconnectWebSocket() {
    if (this.socket) {
      this.socket.disconnect()
      this.socket = null
    }
  }

  /**
   * Subscribe to events
   */
  on(event: string, callback: Function) {
    if (!this.listeners.has(event)) {
      this.listeners.set(event, new Set())
    }
    this.listeners.get(event)?.add(callback)
  }

  /**
   * Unsubscribe from events
   */
  off(event: string, callback: Function) {
    this.listeners.get(event)?.delete(callback)
  }

  /**
   * Emit events
   */
  private emit(event: string, data: any) {
    this.listeners.get(event)?.forEach((callback) => callback(data))
  }

  /**
   * Estimate processing time
   */
  estimateProcessingTime(
    fileSize: number,
    options: ProcessingOptions
  ): number {
    const basetime = 1000 // 1 second base
    const sizeMultiplier = fileSize / (1024 * 1024) // MB
    const qualityMultiplier = {
      low: 0.5,
      medium: 1,
      high: 1.5,
      ultra: 2,
    }[options.quality || 'medium']

    return Math.round(basetime * sizeMultiplier * qualityMultiplier)
  }

  /**
   * Validate image before processing
   */
  validateImage(file: File): { valid: boolean; error?: string } {
    const maxSize = 50 * 1024 * 1024 // 50MB
    const allowedTypes = ['image/png', 'image/jpeg', 'image/webp', 'image/gif']

    if (file.size > maxSize) {
      return { valid: false, error: 'File size exceeds 50MB limit' }
    }

    if (!allowedTypes.includes(file.type)) {
      return { valid: false, error: 'Unsupported file type' }
    }

    return { valid: true }
  }
}

export const processingAPI = new ProcessingAPI()

// Helper functions
export async function processImage(
  file: File,
  options?: ProcessingOptions,
  onProgress?: (progress: number) => void
): Promise<ProcessingResult> {
  return processingAPI.processImage(file, options, onProgress)
}

export async function processBatch(
  files: File[],
  options?: ProcessingOptions
): Promise<BatchJob> {
  return processingAPI.processBatch(files, options)
}

export default processingAPI